Paper
10 October 2023 Deep learning fault diagnosis method for rolling bearings in rolling mills based on improved optimization algorithm
Yuqi Liang, Luan Boyuan
Author Affiliations +
Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127991T (2023) https://doi.org/10.1117/12.3005958
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia
Abstract
In recent years, intelligent fault diagnosis methods based on deep learning have achieved remarkable results in both theoretical research and engineering applications, and rolling bearings are one of the core components of rolling mills, and accurate fault diagnosis of rolling mill bearings can effectively guarantee the safe operation and production efficiency of rolling mill equipment. The current deep learning-based fault diagnosis methods are usually unstable in the training process and the models are difficult to converge, resulting in strong randomness in engineering applications. Optimized algorithms are therefore key to the training of deep learning models. Different optimized algorithms have different effects on the stability of training. The choice of learning rate is also crucial, as a large learning rate may lead to unstable training. In addition, regular techniques can also be used to improve the stability of training. This paper proposes a deep fault diagnosis method for rolling mill bearings based on an improved optimized algorithm, which improves the training efficiency, the stability of the model output results and the robustness of the model with respect to parameter changes while ensuring the diagnostic accuracy of the model.
(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yuqi Liang and Luan Boyuan "Deep learning fault diagnosis method for rolling bearings in rolling mills based on improved optimization algorithm", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127991T (10 October 2023); https://doi.org/10.1117/12.3005958
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KEYWORDS
Mathematical optimization

Education and training

Deep learning

Data modeling

Diagnostics

Convolutional neural networks

Matrices

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